The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.

Learning activities are conducted to help students achieve optimal academic achievement. This research aims to optimize student academic achievement through a learning process that integrates comprehensive formative assessments, including formative tests, self-assessment, peer assessment, and the initiator of creating summaries or concept maps that are given to students in a structured manner at the end of every lesson. The research method used was a quasi-experimental method with a 2x2 factorial design. Students enrolled in the biology education program of the basic physics course for the 1st semester of the 2019 academic year participated in this study. The participants were 66 undergraduate students divided into two classes. Thirty-four students in the experimental group were in class A, while 32 students in the control group were in class B. Data were collected using a learning outcome test instrument to measure academic achievement, which was tested at the end of the semester. Data were analyzed using a two-way ANOVA. This study concluded that a learning process that includes comprehensive formative assessment significantly affects students' academic achievement. These findings support the theory that formative assessment provides feedback, correction, and improvement in student learning.

This study aimed to develop and test a student self-assessment instrument based on the programmatic assessment (PA) components. We applied a series of psychometric research methods by (a) conducting a literature study to find PA constructs, (b) developing the students' self-questionnaires, (c) ensuring content validity, (d) testing face validity, and (e) conducting reliability tests that involve medical students, medical teachers, medical educationalist, and an international PA expert. Face validity (readability test) was conducted with 30 medical students from an Indonesian university who were in their last year of pre-clinical education and had average scores above or equal to their classmates. The confirmatory factor analysis (CFA) was used to report the instruments’ validity and reliability. The final instrument was tested on 121 medical students with excellent GPAs from another medical school with a middle-level accreditation. The PA consists of five components: ‘learning activities’, ‘assessment activities’, 'supporting activities’, 'intermediate evaluations’, and ‘final evaluations'. These components are conveyed through 41 relevant statements with a four-point Likert scale and three yes/no statements. According to the respondents, there was a lack of 'supporting activities' and 'intermediate evaluation' components in the PA in their universities. This study has developed and tested a five-component evaluation instrument based on medical students' perceptions regarding PA implementation.

STEM education is an irreplaceable movement of educational systems across the globe in the 21st century. Both Pre-K, K-12, and higher education institutions consider STEM as an innovative approach to integrate and reform the teaching and learning processes. The purpose of this paper is to examine the development of studies on STEM in the Early Childhood Education context from 1992 to 2022. We investigated a dataset of 308 scholarly works from the Clarivate Web of Science database and figured a diversified collection of research focuses on topics such as children’s readiness, outcomes, teachers’ competency in designing and implementing STEM activities, and the role of computational thinking and robotics. The findings of this paper revealed the dominant contribution of researchers from the USA regarding research quantity and impact, as well as their collaborations with researchers from Western countries. In addition, we also figured out the top influencing authors, documents, and journals as a suggestion for scholars who are new to this topic. However, we would like to note that our findings depended on the quality of the imported database from the WoS system, which covers top-tier journals only.

This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.